Mitigating toxic stress in children affected by conflict and displacement
Bibliographic record
Abstract
Anushka Ataullahjan and colleagues describe the myriad stressors related to conflict and displacement experienced by children and how best to reduce their negative effect Armed conflict and displacement pose a threat to the health and wellbeing of children. As the global community begins to recognize the cumulative effects of conflict and displacement related stressors, our attention has shifted to toxic stress and its short and long term health effects.1 Toxic stress, regarded as the result of prolonged activation of the stress response, can occur before birth and during childhood is known to contribute to epigenetic changes, with health and neurodevelopmental consequences.2 However, various social factors and early and appropriate intervention can help mitigate the negative effects.3 Over 415 million children were living in conflict affected countries in 2018,4 including 33 million displaced children (16 million refugees and asylum seekers, and 17 million internally displaced children).5 Although all children are vulnerable to toxic stress, certain subgroups are particularly vulnerable because of their marginalization—for example, orphaned or unaccompanied children, girls, children with HIV infection, and children with developmental disorders or a disability. These groups may face additional stressors and have reduced access to services. The emergence of covid-19 has also raised concerns about spread in conflict affected populations adding to the risk of toxic stress (box 1). Box 1 ### Covid-19 in humanitarian settings Conflict affected populations are particularly vulnerable to covid-19. Overcrowding and inadequate water and sanitation systems in refugee camps and informal settlements, coupled with previously existing illnesses, may increase the spread and severity of covid-19.67 Moreover, resource and health system constraints may restrict access to adequate and appropriate care.67 Control measures such as physical distancing may be difficult and may also increase economic precarity, intimate partner violence, and food insecurity in populations already vulnerable because of … RETURN TO TEXT
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".